全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
科学通报  2014 

推荐系统托攻击模型与检测技术

DOI: 10.1360/972012-1712, PP. 551-560

Keywords: 推荐系统,协同过滤,托攻击模型,托攻击检测算法

Full-Text   Cite this paper   Add to My Lib

Abstract:

针对协同过滤根据近邻偏好产生推荐的特点,恶意用户注入伪造用户模型成为正常用户近邻,推进或打压目标项目的推荐排名,从而达到改变推荐系统结果,这种攻击方法称为“托攻击”.本文综述了托攻击模型与检测技术的研究现状和面临的主要问题,试图为这一新兴的研究领域勾勒出较为全面清晰的概貌.从推荐系统机理入手,介绍托攻击产生动机、概念、目的、评分向量构成和模型分类,然后提出衡量托攻击对推荐系统危害性的两类指标;接着讨论区分正常用户和托攻击用户的特征指标;然后以机器学习角度分类为主线,综述3类托攻击检测算法,分析3类算法的利与弊,并介绍用于评估托攻击检测算法的数据集、指标和实验方法;最后指出进一步的研究方向.

References

[1]  1 Ricci F, Shapira B. Recommender Systems Handbook. Berlin: Springer, 2011
[2]  2 许海玲, 吴潇, 李晓东, 等. 互联网推荐系统比较研究. 软件学报, 2009, 20: 350-362
[3]  4 Linden G, Smith B, York J. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput, 2003, 7: 76-80
[4]  5 Fang L, Kim H, LeFevre K, et al. A privacy recommendation wizard for users of social networking sites. In: Chen Y, Danezis G, Shmatikov V, eds. Proceedings of the 17th International Conference on Computer and Communications Security. New York: ACM, 2010. 630-632
[5]  10 Cacheda F, Carneiro V, Fernández D, et al. Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Trans Web, 2011, 5: 2
[6]  11 Gunes I, Kaleli C, Bilge A, et al. Shilling attacks against recommender systems: A comprehensive survey. Artif Intell Rev, 2012, doi: 10.1007/s10462-012-9364-9
[7]  12 Williams C, Mobasher B. Profile injection attack detection for securing collaborative recommender systems. Technical Report, Computer Science, DePaul University. 2006
[8]  16 Mehta B, Nejdl W. Unsupervised strategies for shilling detection and robust collaborative filtering. User Model User-Adap, 2009, 19: 65-97
[9]  17 Chirita P, Nejdl W, Zamfir C. Preventing shilling attacks in online recommender systems. In: Bonifati A, Lee D, eds. Proceedings of the 7th International Workshop on Web Information and Data Management. New York: ACM, 2005. 67-74
[10]  18 Burke R, Mobasher B, Williams C, et al. Classification features for attack detection in collaborative recommendation systems. In: Bonifati A, Fundulaki I, eds. Proceedings of the 12th International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2006. 542-547
[11]  21 Mehta B, Hofmann T, Fankhauser P. Lies and propaganda: detecting spam users in collaborative filtering. In: Chin D N, Zhou M X, Lau T A, et al, eds. Proceedings of the 12th International Conference on Intelligent User Interfaces. New York: ACM, 2007. 14-21
[12]  22 Bryan K, O'Mahony M P, Cunningham P. Unsupervised retrieval of attack profiles in collaborative recommender systems. Technical Report, University College Dublin, 2008
[13]  23 Lee J, Zhu D. Shilling attack detection—A new approach for a trustworthy recommender system. Informs J Comput, 2012, 24: 117-131
[14]  30 O'Mahony M P, Hurley N J, Silvestre G C M. Recommender systems: Attack types and strategies. In: Anderson M, Oates T, eds. Proceedings of the 20th National Conference on Artificial Intelligence. USA: MIT Press, 2005. 334-339
[15]  31 Zheng S, Tao J, Baras J S. A robust collaborative filtering algorithm using ordered logistic regression. In: Hagimoto K, Ueda H, Jamallipour A, eds. Proceedings of the 17th International Conference on Communications. New York: IEEE, 2011. 1-6
[16]  32 Su X F, Zeng H J, Chen Z. Finding group shilling in recommendation system. In: Ellis A, Hagino T, eds. Proceedings of the 14th International Conference on World Wide Web. New York: ACM, 2005. 960-961
[17]  35 Lim E, Nguyen V, Jindal N, et al. Detecting product review spammers using rating behaviors. In: Huang J, Koudas N, Jones G J F, et al, eds. Proceedings of the 19th International Conference on Information and Knowledge Management. New York: ACM, 2010. 939-948
[18]  36 Wang G, Xie S H, Liu B, et al. Review graph based online store review spammer detection. In: Cook D J, Pei J, Wang W, et al, eds. Proceedings of the 11th International Conference on Data Mining. New York: IEEE, 2011. 1242-1247
[19]  39 Cheng Z P, Hurley N. Effective diverse and obfuscated attacks on model-based recommender systems. In: Bergman L D, Tuzhilin A, Burke R, et al, eds. Proceedings of the 3rd International Conference on Recommender Systems. New York: ACM, 2009. 141-148
[20]  3 Bell R M, Koren Y. Improved neighborhood-based collaborative filtering. In: Berkhin P, Caruana R, Wu X D, eds. Proceedings of the 13th International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2007. 7-14
[21]  6 Herlocker J L, Konstan J A, Terveen L G, et al. Evaluating collaborative filtering recommender systems. ACM Trans Inform Syst, 2004, 22: 5-53
[22]  7 Lam S K, Riedl J. Shilling recommender systems for fun and profit. In: Feldman S I, Uretsky M, Najork M, et al, eds. Proceedings of the 13th International Conference on World Wide Web. New York: ACM, 2004. 393-402
[23]  8 张富国, 徐升华. 推荐系统安全问题及技术研究综述. 计算机应用研究, 2008, 25: 656-659
[24]  9 王立才, 孟祥武, 张玉洁. 上下文感知推荐系统. 软件学报, 2012, 23: 1-20
[25]  13 Zhang S, Chakrabarti A, Ford J, et al. Attack detection in time series for recommender systems. In: Eliassi-Rad T H, Ungar L, Craven M, et al, eds. Proceedings of the 12th International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2006. 809-814
[26]  14 Hurley N J, Cheng Z P, Zhang M. Statistical attack detection. In: Bergman L D, Tuzhilin A, Burke R, et al, eds. Proceedings of the 3rd International Conference on Recommender Systems. New York: ACM, 2009. 149-156
[27]  15 Liu Q, Chen E H, Xiong H, et al. Enhancing collaborative filtering by user interest expansion via personalized ranking systems. IEEE Trans Syst Man Cy B, 2012, 42: 218-233
[28]  19 Burke R, Williams C, Bhaumik R. Segment-based injection attacks against collaborative filtering recommender systems. In: Han J W, Wah B, eds. Proceedings of the 5th International Conference on Data Mining. New York: IEEE, 2005. 577-580
[29]  20 伍之昂, 庄毅, 王有权, 等. 基于特征选择的推荐系统托攻击检测算法. 电子学报, 2012, 40: 1687-1693
[30]  24 Zhang S, Ouyang Y, Ford J, et al. Analysis of a low-dimensional linear model under recommendation attacks. In: Efthimiadis E N, Dumais S T, Hawking D, et al, eds. Proceedings of the 29th International Conference on Research and Development in Information Retrieval. New York: ACM, 2006. 517-524
[31]  25 李聪, 骆志刚, 石金龙. 一种探测推荐系统托攻击的无监督算法. 自动化学报, 2011, 37: 160-167
[32]  26 Zhou Z H, Li M. Tri-Training: Exploiting unlabeled data using three classifiers. IEEE Trans Knowl Data En, 2005, 17: 1529-1541
[33]  27 Wu Z A, Cao J, Mao B, et al. Semi-SAD: Applying semi-supervised learning to shilling attack detection. In: Mobasher B, Burke R, Jannach D, et al, eds. Proceedings of the 5th International Conference on Recommender Systems. New York: ACM, 2011. 289-292
[34]  28 Cao J, Wu Z A, Mao B, et al. Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system. World Wide Web, 2012, doi: 10.1007/s11280-012-0164-6
[35]  29 Wu Z A, Wu J J, Cao J, et al. HySAD: A semi-supervised hybrid shilling attack detector for trustworthy product recommendation. In: Yang Q, Agarwal D, Pei J, et al, eds. Proceedings of the 18th International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2012. 985-993
[36]  33 Wang Y Q, Wu Z A, Cao J, et al. Towards a tricky group shilling attack model against recommender systems. In: Zhou S G, Karypis G, Zhang S M, eds. Proceedings of the 8th International Conference on Advanced Data Mining and Applications. Berlin: Springer, 2012. 675-688
[37]  34 Mukherjee A, Liu B, Glance N S. Spotting fake reviewer groups in consumer reviews. In: Mille A, Gandon F L, Misselis J, et al, eds. Proceedings of the 21st International Conference on World Wide Web. New York: ACM, 2012. 191-200
[38]  37 Sandvig J J, Mobasher B, Burke R. A survey of collaborative recommendation and the robustness of model-based algorithms. IEEE Data En Bull, 2008, 31: 3-13
[39]  38 Sandvig J J, Mobasher B, Burke R. Robustness of collaborative recommendation based on association rule mining. In: Konstan J A, Riedl J, Smyth B, eds. Proceedings of the 1st International Conference on Recommender Systems. New York: ACM, 2007. 105-112
[40]  40 Mehta B, Hofmann T, Nejdl W. Robust collaborative filtering. In: Konstan J A, Riedl J, Smyth B, eds. Proceedings of the 1st International Conference on Recommender Systems. New York: ACM, 2007, 49-56
[41]  41 Mehta B, Nejdl W. Attack resistant collaborative filtering. In: Myaeng S, Oard W D, Sebastiani F, et al, eds. Proceedings of the 31st International Conference on Research and Development in Information Retrieval. New York: ACM, 2008. 75-82

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133